mlsanityData to 7 Oct 2026

BlindFL: Vertical Federated Machine Learning without Peeking into Your Data

arXiv 2206.07975 ↗First posted 16 Jun 2022cs.LG cs.CRcited by 36

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Abstract (arXiv)

Due to the rising concerns on privacy protection, how to build machine learning (ML) models over different data sources with security guarantees is gaining more popularity. Vertical federated learning (VFL) describes such a case where ML models are built upon the private data of different participated parties that own disjoint features for the same set of instances, which fits many real-world collaborative tasks. Nevertheless, we find that existing solutions for VFL either support limited kinds of input features or suffer from potential data leakage during the federated execution. To this end, this paper aims to investigate both the functionality and security of ML modes in the VFL scenario. To be specific, we introduce BlindFL, a novel framework for VFL training and inference. First, to address the functionality of VFL models, we propose the federated source layers to unite the data from different parties. Various kinds of features can be supported efficiently by the federated source layers, including dense, sparse, numerical, and categorical features. Second, we carefully analyze the security during the federated execution and formalize the privacy requirements. Based on the analysis, we devise secure and accurate algorithm protocols, and further prove the security guarantees under the ideal-real simulation paradigm. Extensive experiments show that BlindFL supports diverse datasets and models efficiently whilst achieves robust privacy guarantees.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 33 of its 84 references that are in the map. First posted on arXiv 16 Jun 2022.

Builds on

33 of 84 references.

References · 33

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2019Deep Leakage from Gradientscited by 1,004
  8. 2019Towards Federated Learning at Scale: System Designcited by 826
  9. 2016Wide & Deep Learning for Recommender Systemscited by 745
  10. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  11. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  12. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
21 more
  1. 2019Deep Learning Recommendation Model for Personalization and Recommendation Systemscited by 327
  2. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  3. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  4. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  5. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  6. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  7. 2021Label Leakage and Protection in Two-party Split Learningcited by 87
  8. 2017CryptoDL: Deep Neural Networks over Encrypted Datacited by 56
  9. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  10. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
  11. 2021OpenBox: A Generalized Black-box Optimization Servicecited by 46
  12. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  13. 2022PaSca: a Graph Neural Architecture Search System under the Scalable Paradigmcited by 37
  14. 2021HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Frameworkcited by 28
  15. 2020Asymmetrical Vertical Federated Learningcited by 26
  16. 2020Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learningcited by 19
  17. 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
  18. 2019Privacy Accounting and Quality Control in the Sage Differentially Private ML Platformcited by 10
  19. 2019SEALion: a Framework for Neural Network Inference on Encrypted Datacited by 9
  20. 2019An Experimental Evaluation of Large Scale GBDT Systemscited by 5
  21. 2021Explore User Neighborhood for Real-time E-commerce Recommendationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 15; 2024: 9; 2025: 5; 2026: 2.

36 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  3. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  4. 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
  5. 2023Practical Differentially Private and Byzantine-resilient Federated Learningcited by 18
  6. 2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
  7. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
  8. 2023On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlookcited by 15
  9. 2022Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learningcited by 13
  10. 2022Vertical Semi-Federated Learning for Efficient Online Advertisingcited by 12
  11. 2023FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscationcited by 9
  12. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
24 more
  1. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  2. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  3. 2023Secure Vertical Federated Learning Under Unreliable Connectivitycited by 4
  4. 2023Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofscited by 4
  5. 2022Protecting Split Learning by Potential Energy Losscited by 3
  6. 2023Efficient Vertical Federated Learning with Secure Aggregationcited by 3
  7. 2024URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learningcited by 3
  8. 2025The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communicationscited by 3
  9. 2023Defending Label Inference Attacks in Split Learning under Regression Settingcited by 2
  10. 2025Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approachcited by 2
  11. 2023FedST: Secure Federated Shapelet Transformation for Time Series Classificationcited by 1
  12. 2023A Distributed Privacy Preserving Model for the Detection of Alzheimer's Diseasecited by 1
  13. 2024TabVFL: Improving Latent Representation in Vertical Federated Learningcited by 1
  14. 2024PackVFL: Efficient HE Packing for Vertical Federated Learningcited by 1
  15. 2024EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspectioncited by 1
  16. 2023Horizontal Federated Computer Visioncited by 0
  17. 2024Vertical Federated Image Segmentationcited by 0
  18. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
  19. 2024Enhancing Data Provenance and Model Transparency in Federated Learning Systems -- A Database Approachcited by 0
  20. 2025Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarioscited by 0
  21. 2025WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Siloscited by 0
  22. 2025VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Maskingcited by 0
  23. 2026SecureSplit: Mitigating Backdoor Attacks in Split Learningcited by 0
  24. 2026Secure and Privacy-Preserving Vertical Federated Learningcited by 0